Paper Info
Title
A Novel Alternative Exponent-Weighted Fuzzy C-Means Algorithm
Abstract
Under noisy environment and uneven data distribution, Fuzzy C-Means (FCM) algorithm and some of its advanced algorithms give large miss-clustering result or become malfunction. This paper proposes a novel Alternative Exponent-weighted Fuzzy C-Means (AEFCM) algorithm which introduces exponent-weight matrix and defines a new metric space. During iteration, the exponent-weight matrix gives every data sample a difference weight based on difference cluster center. Meanwhile, new metric space can efficiently restrain the bad influence produced by noisy samples during the iteration. Experiments have proved that AEFCM algorithm may overcome the bugs of FCM algorithm in a certain extent, with favorable convergence and robustness.
Year
DOI
Venue
2013
10.1109/GreenCom-iThings-CPSCom.2013.325
GreenCom/iThings/CPScom
Keywords
Field
DocType
noisy sample,aefcm,robustness,fuzzy clustering,fuzzy set theory,difference weight,pattern clustering,alternative exponent weighted fuzzy c-means algorithm,data sample,difference cluster center,statistical analysis,advanced algorithm,new metric space,fuzzy c-means,metric space,matrix algebra,fcm,noisy environment,exponent-weight matrix,exponent-weighted,convergence,aefcm algorithm,iteration method,c-means algorithm,data distribution,fcm algorithm,iterative methods,novel alternative exponent-weighted fuzzy
Fuzzy clustering,Mathematical optimization,Fuzzy classification,Computer science,Fuzzy logic,Algorithm,Robustness (computer science),Fuzzy set,FLAME clustering,Metric space,Fuzzy number
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Renhao Fan100.34
Xiang Wang22615.33
Jordi Madrenas315027.87